Movatterモバイル変換

[0]ホーム
URL:

Skip to main content
Springer Nature Link
Log in

Cross-Layer Optimized Call Admission Control in Cognitive Radio Networks

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

In Cognitive Radio (CR) networks, Call Admission Control (CAC) is a key enabling technique to ensure Quality-of-Service (QoS) provisioning for Secondary Users (SUs). CAC decisions are usually made based on the current traffic volume in the system. However, in CR networks, the system state of channel utilization can only be partially observed through spectrum sensing. The presence of sensing error may mislead the CAC strategy to make an inefficient or even incorrect decision. To achieve QoS provisioning in CR networks, a practical CAC strategy should have in-built functionality to deal with the inaccuracy of sensing results. This paper is motivated to construct a cross-layer optimization framework, in which the parameters of CAC strategy and spectrum sensing scheme are simultaneously tuned to minimize the dropping rate while satisfying the requirements of both blocking rate and interference threshold. After introducing a multiple-stair Markov model to approximate the non-memoryless state transitions, the cross-layer optimization is modelled as a non-linear programming problem. The method of branch-and-bound is employed to solve the problem, where five components are involved: problem selection, reformulation linear technique, simplex method, local search and sub-problem generation. Extensive simulations are carried out to evaluate the proposed CAC strategy. The simulation results show that our CAC strategy significantly outperforms two traditional strategies. The dropping rate in our strategy is considerably reduced. Meanwhile, the blocking rate and the interference probability strictly coincide with the constraints.

This is a preview of subscription content,log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Notes

  1. Here, the time durationts does not always equal to the sensing time of each channel. For instance, in [12], channels are divided into batches. Channels of a same patch are sensed simultaneously. If there aren patches, the exact sensing time for each individual channel is given by\(\frac{t_s}{n}\).

  2. Note that,d = − 1 means that the new SUs is admitted. In this situation, parameterd should be reexplained as the change of the number of SUs, but not the number of dropped SUs.

References

  1. Akyildiz IF, Lee WY, Vuran MC, Mohantly S (2006) Next generation/dynamic spectrum access/cognitive radio wireless network: a survey. Comput Networks 50(13):2127–2159

    Article MATH  Google Scholar 

  2. Mitola J, Maguire GQ (1999) Cognitive radio: making software radios more personal. IEEE Pers Commun 6(4):13–18

    Article  Google Scholar 

  3. Ramjee R, Nagarajan R, Towsley D (1997) On optimal call admission control in cellular network. Wirel Netw (Springer) 3(1):29–41

    Article  Google Scholar 

  4. Falowo OE, Chan HA (2007) Adaptive bandwidth management and joint call admission control to enhance system utilization and QoS in heterogeneous wireless networks. EURASIP J Wirel Commun Netw

  5. Yu F, Krishnamurthy V, Leung VCM (2006) Cross-layer optimal connection admission control for variable bit rate multimedia traffic in packet. IEEE Trans Signal Process 54(2):542–555

    Article  Google Scholar 

  6. Zhu X, Shen L (2007) Analysis of cognitive radio spectrum access with optimal channel reservation. IEEE Commun Lett 11(4):304–306

    Article MathSciNet  Google Scholar 

  7. Zhang L, Liang YC, Xin Y (2007) Joint admission control and power allocation for cognitive radio networks. In: ICASSP 2007, vol 3, pp 673–676

  8. Kim H, Shin KG (2007) Efficient discovery of spectrum opportunities with MAC-layer sensing in cognitive radio networks. IEEE Trans Mob Comput 7:533–545

    Article  Google Scholar 

  9. Lee WY, Akyildiz IF (2008) Optimal spectrum sensing framework for cognitive radio networks. IEEE Trans Wirel Commun 7(10):3845–3857

    Article  Google Scholar 

  10. Jia J (2008) HC-MAC: a hardware-constrained cognitive MAC for efficient spectrum management. IEEE J Sel Areas Commun 26(1):106–117

    Article  Google Scholar 

  11. Ganesan G (2007) Cooperative spectrum sensing in cognitive radio. Part I: two user networks. IEEE Trans Wirel Commun 6(6):2204–2213

    Article  Google Scholar 

  12. Liu Y, Yu R, Xie S (2008) Optimal cooperative sensing scheme under time-varying channel for cognitive radio networks. In: IEEE dyspan’08

  13. Valaee S, Li B (2004) Distributed call admission control for ad hoc networks. IEEE Trans Wirel Commun 11(4):6–14

    Article  Google Scholar 

  14. Zamat H, Natarajan B (2008) Use of dedicated broadband sensing receiver in cognitive radio. In: IEEE international conference on communications workshops, 2008. ICC workshops’08

  15. Timed markov models.http://www.mathpages.com/HOME/kmath589/kmath589.htm

  16. Sherali HD, Adams WP (1999) A reformulation-linearization technique for solving discrete and continuous nonconvex problems, chapter 8. Kluwer, Dordrecht

    Google Scholar 

  17. Digham F, Alouini M, Simon M (2007) On the energy detection of unknown signals over fading channels. IEEE Trans Commun 55(1):21–24

    Article  Google Scholar 

  18. Tewfik AH, Kim M (1994) Fast positive definite linear system solvers. IEEE Trans Signal Process 42(3):572–582

    Article  Google Scholar 

  19. Orlin JB (1996) A polynomial time primal network simplex algorithm for minimum cost flows. In: Proceedings of the seventh annual ACM-SIAM symposium on discrete algorithms, pp 474–481

Download references

Acknowledgements

The work in this paper is supported by Key Programs of NSFC with Grant No. U0635001, U0828003.

Author information

Authors and Affiliations

  1. School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China

    Rong Yu, Ming Huang & Shengli Xie

  2. Simula Research Laboratory, Fornebu, Norway

    Yan Zhang

Authors
  1. Rong Yu

    You can also search for this author inPubMed Google Scholar

  2. Yan Zhang

    You can also search for this author inPubMed Google Scholar

  3. Ming Huang

    You can also search for this author inPubMed Google Scholar

  4. Shengli Xie

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence toRong Yu.

Rights and permissions

About this article

Access this article

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Advertisement

[8]ページ先頭
©2009-2025 Movatter.jp